惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

博客园 - 【当耐特】
云风的 BLOG
云风的 BLOG
罗磊的独立博客
C
Check Point Blog
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Blog — PlanetScale
Blog — PlanetScale
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
月光博客
月光博客
大猫的无限游戏
大猫的无限游戏
Google DeepMind News
Google DeepMind News
Engineering at Meta
Engineering at Meta
N
Netflix TechBlog - Medium
宝玉的分享
宝玉的分享
Recent Announcements
Recent Announcements
酷 壳 – CoolShell
酷 壳 – CoolShell
博客园_首页
J
Java Code Geeks
Apple Machine Learning Research
Apple Machine Learning Research
人人都是产品经理
人人都是产品经理
爱范儿
爱范儿
I
InfoQ
Hugging Face - Blog
Hugging Face - Blog
T
Tailwind CSS Blog
B
Blog RSS Feed

cs.SI updates on arXiv.org

Hiding in Plain Sight: Finding MAHA on Reddit Prism: Structural Symmetry Scanning via Duality-Constrained Laplacian Projection MV-Gate: Insider Threat Detection via Multi-View Behavioral Statistics and Semantic Modeling Algorithmic Cultivation: How Social Media Feeds Shape User Language Universal Dynamics of Punctuated Progress AI-Mediated Communication Can Steer Collective Opinion CitePrism: Human-in-the-Loop AI for Citation Auditing and Editorial Integrity Explainable Detection of Depression Status Shifts from User Digital Traces Can Visual Mamba Improve AI-Generated Image Detection? An In-Depth Investigation ScioMind: Cognitively Grounded Multi-Agent Social Simulation with Anchoring-Based Belief Dynamics and Dynamic Profiles Humanwashing -- It Should Leave You Feeling Dirty When Do LLMs Generate Realistic Social Networks? A Multi-Dimensional Study of Culture, Language, Scale, and Method Moltbook Moderation: Uncovering Hidden Intent Through Multi-Turn Dialogue Linking Extreme Discourse to Structural Polarization in Signed Interaction Networks Predicting Channel Closures in the Lightning Network with Machine Learning Latent Causal Void: Explicit Missing-Context Reconstruction for Misinformation Detection Predictive Maps of Multi-Agent Reasoning: A Successor-Representation Spectrum for LLM Communication Topologies Large Language Models for Causal Relations Extraction in Social Media: A Validation Framework for Disaster Intelligence When Can Digital Personas Reliably Approximate Human Survey Findings? RAwR: Role-Aware Rewiring via Approximate Equitable Partition GravityGraphSAGE: Link Prediction in Directed Attributed Graphs Structure-Centric Graph Foundation Model via Geometric Bases Attention-based graph neural networks: a survey When AI Meets Science: Research Diversity, Interdisciplinarity, Visibility, and Retractions across Disciplines in a Global Surge Scalable inference of spatial regions and temporal signatures from time series Can LLMs Emulate Human Belief Dynamics? Predicting Post Virality with Temporal Cross-Attention over Trend Signals H3: A Healthcare Three-Hop Index for Physician Referral Network Prediction Dynamic Graph with Similarity-Aware Attention Graph Neural Network for Recommender Systems Spectral Graph Sparsification Preserves Representation Geometry in Graph Neural Networks
Detailed temporal structure of communication networks in ...
Dan Stowell, Lisa Gill, David Clayton · 2016-01-21 · via cs.SI updates on arXiv.org

Animals in groups often exchange calls, in patterns whose temporal structure may be influenced by contextual factors such as physical location and the social network structure of the group. We introduce a model-based analysis for temporal patterns of animal call timing, originally developed for networks of firing neurons. This has advantages over cross-correlation analysis in that it can correctly handle common-cause confounds and provides a generative model of call patterns with explicit parameters for the influences between individuals. It also has advantages over standard Markovian analysis in that it incorporates detailed temporal interactions which affect timing as well as sequencing of calls. Further, a fitted model can be used to generate novel synthetic call sequences. We apply the method to calls recorded from groups of domesticated zebra finch (Taenopyggia guttata) individuals. We find that the communication network in these groups has stable structure that persists from one day to the next, and that "kernels" reflecting the temporal range of influence have a characteristic structure for a calling individual's effect on itself, its partner, and on others in the group. We further find characteristic patterns of influences by call type as well as by individual.